SKILLEMALL.ai

AB hal-vault

Securely store, search, and use secrets (API keys, tokens, passwords, SSH keys) with hal-vault, an SSH-key encrypted local secret store. Use when the user shares a credential that should be saved, asks what secrets are stored or where a key is, or when a command/workflow needs a secret injected. Core discipline - never print raw secret values into chat, logs, or files; reference secrets only by their masked form, and use --reveal exclusively inside command substitution.

ClawHub Agent Skills author: OFOX AI v1.1.0 MIT-0 4 files body ≈ 1 367 tokens Open the sourceclawhub.ai analyzed 2 d ago

Securely store, search, and use secrets (API keys, tokens, passwords, SSH keys) with hal-vault, an SSH-key encrypted local secret store.

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationGitHubAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 65/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 6 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 12 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1367 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 474: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This is a coherent local secret-vault skill, but it needs review because its instructions may expose real credentials in command transcripts and install an unpinned credential-handling binary.
    LLM: suspicious (high) · 8 Sept 2026